基于生成和情感反映的记忆更新：创建KEEM数据集以改善长期对话
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation
AI总结：
KEEM数据集通过动态生成整合性记忆，结合情感和关键信息，提升长期对话系统对用户状态的理解与回应能力。
摘要：
在本工作中，我们介绍了Keep Emotional and Essential Memory（KEEM）数据集，一个新型的基于生成的数据集，旨在增强长期对话系统中的记忆更新。与现有方法依赖简单累积或操作方法不同，后者常导致信息冲突和难以准确跟踪用户当前状态，KEEM动态生成整合性记忆。此过程不仅保留了关键事实信息，还融合了情感上下文和因果关系，使系统能更细致地理解用户交互。通过无缝更新系统记忆，将情感和关键数据相结合，我们的方法促进了更深层次的同理心，并增强了系统在开放领域对话中作出有意义回应的能力。
英文摘要：
In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user's current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system's memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system's ability to respond meaningfully in open-domain conversations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
